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AutoML Performance Boxplot

Features Importance (Original Scale)

Scaled Features Importance (MinMax per Model)

Spearman Correlation of Models

Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.9 seconds
Metric details:
| Metric |
Score |
| MAE |
108.41 |
| MSE |
20099.5 |
| RMSE |
141.773 |
| R2 |
0.738506 |
| MAPE |
0.166832 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 16_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
15.9 seconds
Metric details:
| Metric |
Score |
| MAE |
101.996 |
| MSE |
17886.1 |
| RMSE |
133.739 |
| R2 |
0.767302 |
| MAPE |
0.161217 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 10_LightGBM |
3 |
| 12_LightGBM |
1 |
| 14_CatBoost |
2 |
| 16_CatBoost |
2 |
| 2_Default_Xgboost |
1 |
| 3_Default_CatBoost |
5 |
| 7_Xgboost |
1 |
| 8_Xgboost |
1 |
Metric details:
| Metric |
Score |
| MAE |
101.189 |
| MSE |
17634.8 |
| RMSE |
132.796 |
| R2 |
0.770571 |
| MAPE |
0.160786 |
Learning curves

True vs Predicted

Predicted vs Residuals

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Summary of 11_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.9 seconds
Metric details:
| Metric |
Score |
| MAE |
104.139 |
| MSE |
18564.4 |
| RMSE |
136.251 |
| R2 |
0.758477 |
| MAPE |
0.163691 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 10_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
12.7 seconds
Metric details:
| Metric |
Score |
| MAE |
102.042 |
| MSE |
17910.6 |
| RMSE |
133.831 |
| R2 |
0.766983 |
| MAPE |
0.161852 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 3_Default_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.1 seconds
Metric details:
| Metric |
Score |
| MAE |
101.73 |
| MSE |
17821.2 |
| RMSE |
133.496 |
| R2 |
0.768146 |
| MAPE |
0.161371 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 8_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 7
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.9 seconds
Metric details:
| Metric |
Score |
| MAE |
102.972 |
| MSE |
18226.3 |
| RMSE |
135.005 |
| R2 |
0.762876 |
| MAPE |
0.163192 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 22_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 4
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.4 seconds
Metric details:
| Metric |
Score |
| MAE |
108.559 |
| MSE |
20118.3 |
| RMSE |
141.839 |
| R2 |
0.738261 |
| MAPE |
0.167924 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 23_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.9 seconds
Metric details:
| Metric |
Score |
| MAE |
108.261 |
| MSE |
20011.6 |
| RMSE |
141.462 |
| R2 |
0.739649 |
| MAPE |
0.167118 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 2_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.9 seconds
Metric details:
| Metric |
Score |
| MAE |
102.541 |
| MSE |
18111.5 |
| RMSE |
134.579 |
| R2 |
0.764369 |
| MAPE |
0.161782 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 14_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
27.7 seconds
Metric details:
| Metric |
Score |
| MAE |
101.895 |
| MSE |
17854.5 |
| RMSE |
133.621 |
| R2 |
0.767713 |
| MAPE |
0.161677 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 15_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 1.0
- loss_function: MAE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
14.4 seconds
Metric details:
| Metric |
Score |
| MAE |
102.889 |
| MSE |
18177.1 |
| RMSE |
134.822 |
| R2 |
0.763516 |
| MAPE |
0.162411 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 6_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.8 seconds
Metric details:
| Metric |
Score |
| MAE |
103.439 |
| MSE |
18425.2 |
| RMSE |
135.739 |
| R2 |
0.760288 |
| MAPE |
0.162912 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 18_RandomForest
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Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
23.8 seconds
Metric details:
| Metric |
Score |
| MAE |
124.897 |
| MSE |
25860.9 |
| RMSE |
160.813 |
| R2 |
0.663549 |
| MAPE |
0.183888 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 12_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
12.8 seconds
Metric details:
| Metric |
Score |
| MAE |
102.6 |
| MSE |
18095.2 |
| RMSE |
134.518 |
| R2 |
0.764582 |
| MAPE |
0.161761 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
11.6 seconds
Metric details:
| Metric |
Score |
| MAE |
123.849 |
| MSE |
25587.6 |
| RMSE |
159.961 |
| R2 |
0.667106 |
| MAPE |
0.181633 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 7_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 8
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.8 seconds
Metric details:
| Metric |
Score |
| MAE |
103.627 |
| MSE |
18464.7 |
| RMSE |
135.885 |
| R2 |
0.759774 |
| MAPE |
0.163594 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 1_Default_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
16.8 seconds
Metric details:
| Metric |
Score |
| MAE |
102.695 |
| MSE |
18158.3 |
| RMSE |
134.753 |
| R2 |
0.76376 |
| MAPE |
0.161753 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 19_RandomForest
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Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.7
- min_samples_split: 50
- max_depth: 3
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
15.3 seconds
Metric details:
| Metric |
Score |
| MAE |
131.02 |
| MSE |
28256.8 |
| RMSE |
168.098 |
| R2 |
0.632379 |
| MAPE |
0.189525 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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